energies

Review

Technology is not a Barrier: A Survey of EnergySystem Technologies Required for InnovativeElectricity Business Models Driving the Low CarbonEnergy Revolution

Christoph Mazur 1,2,* , Stephen Hall 3, Jeffrey Hardy 2 and Mark Workman 4

1 Chemical Engineering Department, Imperial College London, London SW7 2AZ, UK2 Grantham Institute—Environment and Climate Change, Imperial College London, London SW7 2AZ, UK;

jeff.hardy@imperial.ac.uk3 School of Earth and Environment, University of Leeds, Leeds LS2 9JT, UK; S.Hall@leeds.ac.uk4 Energy Systems Catapult, Birmingham B4 6BS, UK; mark.workman07@imperial.ac.uk* Correspondence: christoph.mazur@imperial.ac.uk

Received: 26 November 2018; Accepted: 24 January 2019; Published: 29 January 2019�����������������

Abstract: Energy system decarbonisation and changing consumer behaviours will create and destroynew markets in the electric power sector. This means that the energy industry will have to adapttheir business models in order to capture these pools of value. Recent work explores how changesto the utility business model that include digital, decentralised or service-based offers could bothdisrupt the market and accelerate low carbon transitions. However, it is unclear whether thesebusiness models are technologically feasible. To answer this question, we undertook an expert panelstudy to determine the readiness levels of key enabling technologies. The result is an analysis ofwhat technologies may hinder electricity business model innovation and where more research ordevelopment is necessary. The study shows that none of the business models that are compatiblewith a low carbon power sector are facing technology barriers that cannot be overcome, but there isstill work to be done in the domain of system integration. We conclude that, especially in the field ofenergy system coordination and operation, there is a need for comprehensive demonstration trialswhich can iteratively combine and test information and communications technology (ICT) solutions.This form of innovation support would require a new approach to energy system trials.

Keywords: technology review; electricity system; future service energy business models; technologyreadiness level; energy revolution

1. Introduction

In recent years, the electricity system of the United Kingdom underwent major changes. The drivetoward a lower carbon mix, together with the digitalisation and decentralisation of the system [1] andchanged consumer behaviour and expectations, are challenging all the aspects of traditional systemoperation (National Grid, 2018). This leads to a variety of different future transition pathways [2,3].It is not clear yet whether the transition will lead to a more centralized CCS (carbon capture andstorage) or nuclear-based system, or to a completely decentralized renewables-based system withexpanded flexibility and changed consumer behaviour—to name just a few options [4,5].

What is clear is that a transition to any of these futures will create and destroy different types ofvalues currently captured by energy market participants [4,5]. This means that current energy marketplayers will have to change the way they operate in order to be able to capture these new values.

Energies 2019, 12, 428; doi:10.3390/en12030428 www.mdpi.com/journal/energies

Energies 2019, 12, 428 2 of 13

To explore the effect these changes may have on the utility business model, the Utility 2050 project [4]studied what electricity system value pools there may be in 2050 [5], and how these could be capturedthrough the help of novel business models in 2016. Business models are understood not as a financialproposition or profit model, but as a frameworks to understand, evaluate and compare how businessescreate, deliver and capture value [6,7].

Energy markets are theoretically open to any form of business model that can satisfy licensecondition, but other markets, economies of scale, increasing competencies and efficiency returns [8,9]led a few similar business to dominate,. In the energy market, the corporate volume sale utility becamethe incumbent model [2,10]. Recent works on energy business models address how pressures createdin other parts of the energy system, e.g. technological change, climate change commitments andevolving consumer preferences, place pressure on incumbent business models to evolve [11,12].

These studies greatly improved our understanding of the co-evolving nature of business modelswith the rest of the energy system [13], but have done little to appraise or assess the bundle oftechnologies that either allow them to exist, or those upstream technologies that can accommodatethem into the existing market. This is a problem because there is no system level assessment of whethertechnological readiness is a fundamental barrier to rapid system decarbonisation led by new utilitybusiness models.

This paper undertakes this assessment by matching extant and proposed technologies of a suite ofinnovative business models generated by a stakeholder coproduction exercise led by the Utility 2050 project.In summer 2016, the Utility 2050 project convened 40 stakeholders from the UK energy sector to generatea suite of 11 new utility business models that could accelerate system decarbonisation, address the changingdemands of the sector and operate in a recognisable liberalised market. These 11 business models wereshortlisted to five by selecting those which were most compatible with the UK case and the current stage ofmarket liberalisation.

This paper describes how the technologies most critical for each business model were assessedby our expert panel study. Then, it provides an overview of the maturity of these technologies usingthe Technology Readiness Level (TRL) framework. Finally, it shows which of the technologies couldhinder the realisation of the business model archetypes outlined in Section 2.2.

2. Material and Methods

In this study, we applied an iterative approach to identify the maturity of the technologiesrequired for nontraditional business models (see Section 2.2) that may emerge in future energy systems.The technologies were derived from a set of novel business models that emerged from the stakeholderworkshop described above. Technology needs for each business model were explicitly requested fromworkshop participants for each proposed business model suggested. These technology needs wereextracted from workshop feedback and internal extrapolation. For each of these technologies, we usedthe Technology Readiness Level (TRL) (see Section 2.1) approach to determine the current maturityof the technologies. Similarly to Reddy and Painuly (2004) [14], we recruited an expert panel [15]of energy, electricity system stakeholders and researchers to undergo a process of assigning TRLsto the different technologies (see Section 2.3 for survey design and respondents). In contrast to paststudies [16,17] that looked into different types of barriers, this study focused on technological barriersonly. Also, the expert panel paid particular attention in subsequent rounds of analysis to key enablingtechnologies in lower TRL bands. For these technologies, the panel also analysed how crucial eachtechnology was as an enabling element of each discrete business model.

2.1. Technology Readiness Levels

The critical gap this study addresses is that, to our knowledge, there is no comparable assessmentof the readiness of the basket of technologies needed to make novel future electricity utility businessmodels work, as opposed to only those that enable a specific market function. There is no maturityoverview on technologies for future energy systems and energy business models for electricity markets.

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Limited by time and resources and by confidentiality of the industry, this study fills this gap usingan elite sampling expert panel approach.

In this study, we chose the TRL concept as the assessment tool for the technologies. Introducedby NASA in the 1970s, the TRL concept is a way to have a discipline-independent assessment andcommunication of the maturity of new technologies. In 1995, Mankins articulated the first definitionsof all of the nine levels, with TRL 1 being “basic principles observed and reported” and TRL 9 “actualsystem ‘flight proven’ through successful mission operations” [18]. The concept was applied in manydifferent segments and is well-understood in different disciplines [19]. In our study, we used theEuropean Comission’s Horizon 2020 working definitions [20]. To reduce complexity, we groupedthe nine categories into three categories (see Table 1): Laboratory/Research (TRL 1–3), Demonstratorstatus (TRL 4–6) and Deployment status (TRL 7–9), with the latter being classes as a maturity in whichroll out was largely dependent on exogenous market conditions.

Table 1. Technology Readiness Levels (TRL) categories provided to survey respondents (in comparisonto EU Horizon 2020 categories).

TRL Categories used for thisstudy’s survey. EU Horizon 2020 TRL categories [20]

1-3 Laboratory/ResearchTRL 1—basic principles observedTRL 2—technology concept formulatedTRL 3—experimental proof of concept

4-6 Demonstrator status

TRL 4—technology validated in labTRL 5—technology validated in relevant environment (industriallyrelevant environment in the case of key enabling technologies)TRL 6—technology demonstrated in relevant environment (industriallyrelevant environment in the case of key enabling technologies)

7–9 Deployment status

TRL 7—system prototype demonstration in operational environmentTRL 8—system complete and qualifiedTRL 9—actual system proven in operational environment (competitivemanufacturing in the case of key enabling technologies; or in space)

2.2. Business Model Archetypes for the Future Energy System

The technologies were identified based upon a set of business model archetypes producedby the Utility2050 project [4]. The attendees of the workshop were provided with a set of futureenergy scenarios, ranging from centralized CCS-based future to highly decentralized renewable-basedscenarios based upon a past study [5].

Five business model archetypes were developed in detail in the workshop.Low carbon generators, who focus on the building and provision of low carbon capacity

(with potentially CCS) in order to sell the capacity or power directly to large customers or the wholesalemarket. This business model archetype is designed to address the wholesale and capacity marketsdirectly. It has no consumer offer and focuses on winning subsidy payment, avoids carbon pricing,and operates an efficient wholesale model with some commercial to commercial direct contracting.

New Electrifiers, a ‘volume sales’ utility that is helping consumers switch to electric heat andmobility in order to significantly expand demand from commercial and domestic customers. It alsoincludes installing equipment, such as storage or photovoltaic (PV) and automating Demand-sideResponse (DSR).

Energy Service Companies (ESCo), offers the ESCo value proposition to private consumers anddelivers energy services to customers, such as comfort and illumination, rather than units of energy likea traditional supplier. The ESCo sources the residual energy demand from either its own generationor the wholesale market. Importantly, the utility may also lease the vehicle and battery througha single energy service bill and charge for annual mileage, utilising the vehicle battery to optimise theconsumer’s consumption for different outcomes, i.e. low carbon or least cost. There is strong DSM

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capability and the majority of balancing services move to demand response through vehicle to gridand appliance based demand side management.

Peer-to-Peer Trading allows customers to directly buy, sell or swap electricity with each other.Third Party Controllers, similar to the ESCo, but also bundles all other utility services (water,

communication, etc.) combined with switching between services on-behalf of the customer.These five business model archetypes [4] are outlined Figure 1.

Figure 1. Future Business Models.

2.3. The Expert Panel and Survey Design

We constructed an expert panel from UK stakeholders (see Table 2) to inform an iterative surveyapproach. The expert panel method is particularly suited to foresight situations, in which a complexproblem with uncertain boundaries is in question, where credibility of personal judgement is importantand with whom a medium term discursive or iterative process is desirable. The panel was presentedwith an enabling technologies matrix (see Tables 3–5), and asked to execute the following tasks:

1. Where they have knowledge on the technology, assign one of the three TRL category (see thecategories in Table 1) for the technologies in Tables 3–5. For technologies out of their scope weasked to provide no input.

2. Where TRL category 1—3 or 4—6 was selected, we asked them to assess how the level of difficultyto get the technologies to TRL 7—9 and to comment on this. The panel was asked to mark thebarriers as “low,” “medium” or “high,” and to clarify the barrier. The clarification ensured thatthe respondents did not assign TRL levels based upon non-technology aspects

We then gathered the responses and collated the inputs for all of the technologies in the threeTRL categories.

This elite sampling method allowed us to investigate TRL from an informed cross section ofthe sector. Finally, we linked these technologies back to the business model archetypes to outlinewhich of the technologies and solutions that are not yet ready for deployment need further work anddevelopment, or at least deployment.

Our expert panel was asked to limit their assessment to pure technology readiness and wereexplicitly informed not to consider cost, policy compatibility or regulatory limitations. This isspecifically to represent the technology enablers of innovative business models which in themselvesare market destabilising and, therefore, reflecting on issues outside the ability of the technology towork in its intended situation would be unhelpful to the stated question of this study.

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This study aimed to explore the pivotal nature of the engineering and ICT required for low-carbonutility business models, not how the business models perform within a given power market. The onlycriterion under consideration was whether the technology was already deployed anywhere successfully,either in Research and Development (R&D), subsidised trial or near-market applications. This studyaimed to capture whether the technology was already deployed on a reasonable scale or whetherit was still in the R&D stage. Clearly, this provides a space for different interpretations. Therefore,we invited a variety of experts to take part in this survey (see Table 2) to mitigate this risk. We alsoacknowledge that the sample of experts may be limited. However, it represents high-level engagementfrom an elite audience in a medium-term research process. In a future work, the panel may beexpanded to a wider membership.

Table 2. Overview of experts who contributed to the technology survey.

Technical Lead in energy optimisation and integration ofrenewable energy

Industry (EU, Large enterprise inpower systems)

CTO of company in the domain of smart grid technology, control,storage and grid services

Industry (UK SME in smartgrid tech)

Energy Engineer with expertise in energy systems, heat pumps,Combined Heat and Power (CHP) and energy services

Industry (UK Large Enterprise inengineering consultancy)

Smart Grid Analyst and expert in Smart Grids, renewable energy,energy markets, and network transition

Industry (UK SME in smartgrid Solutions)

Principal Sustainability Advisor and Build Environment Energy expert Institute in Buildings (UK)

Senior Manager in Sustainable Energy, and expert in decentralizedenergy systems Municipality (UK)

Research Fellow in Smart and Sustainable District, decentralized energysystems, smart meter solutions and ICT infrastructures Academia (UK)

Research Fellow in statistical learning and control for electricity grids.Smart Grid technologies Academia (UK)

Professor in urban systems, energy systems and technologies andinfrastructures Academia (UK)

Reader in Clean Energy Processes and expert in energy efficiency andrenewable technologies Academia (UK)

Research Fellow in Energy Systems and the Built Environment. Expertin energy management in non-domestic and domestic sectors Academia (UK)

Senior lecturer in energy systems models and energy technology,infrastructure, economics and the environment. Academia (UK)

Research Fellow in economics and policy for Climate ChangeMitigation Technologies Academia (UK)

3. Results

In this section, we provide the results of our maturity assessment of technologies are key enablersfor low-carbon business model innovation in the utility sector—here in the case of the electricity system.We split the technologies into three different groups: Supply-side technologies, demand-side technologies,and technologies that are needed in the domain of data, communication and integration. Where there arelow levels of TRL and a need for further work, a more detailed description of the technology is provided.

3.1. Technology Readiness Levels of Energy System Technologies

3.1.1. Supply Side Technologies

Table 3 provides an overview of the supply technologies that have been outlined as the keytechnologies to enable the different business models. Most of them are technologies in the domain

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of generation of plant management. The results show that all technologies are seen to be at the stagewhere they can be widely deployed.

Table 3. TRLs of supply-side technologies.

Energy supply andgeneration technologies

(Numbers indicate for whichbusiness models these are

relevant: Low carbon generators(1), New Electrifiers (2), EnergyService Companies (3), Peer toPeer platform (4), Third Party

Controllers (5).

Explanation(with literature reference for

not widelyknown technologies)

Technology Readinesslevel (TRL)

(Colour Code outlinesTRL level)

1–3 red4–6 amber7–9 green

Constraints/Barriers/Limitsto increase TRL to 7–9

(Colour code illustrates howeasy to overcome)simple—green

medium—amberdifficult—red

Stationary Batteries(1, 2, 3, 4, 5)

Large scale stationary storagetechnologies connected to the

grid, to provide storage aswell as flexibility services.

7–9 Not relevant as already highestTRL range

Micro Combined Heat andPower (CHP)(1, 2, 3, 4, 5)

Combined heat and powergeneration for

residential applications [21]7–9 Not relevant as already highest

TRL range

Solar PV(1, 2, 3, 5) - 7–9 Not relevant as already highest

TRL rangeSolar Thermal

(1, 2, 3, 5) - 7–9 Not relevant as already highestTRL range

Heat Storage(1, 2, 3, 5)

Different types of heat storageincluding Phase Change

Material (PCM) [21]7–9 Not relevant as already highest

TRL range

Fuel Cells(1, 2, 3) - 7–9 Not relevant as already highest

TRL rangeCarbon Capture and Storage

(1) - 7–9 Not relevant as already highestTRL range

Synthetic Fuels(1, 2, 3)

Synthetic fuels that allow thestorage of electricity in other

chemical forms [22]7–9 Not relevant as already highest

TRL range

District Heat Networks(1, 2, 3, 5)

4th generationheat network [23] 7–9 Not relevant as already highest

TRL rangeGas Fired Power plants

(1, 2, 3, 5) - 7–9 Not relevant as already highestTRL range

Diesel generators(1, 2, 3, 5) - 7–9 Not relevant as already highest

TRL range

Interconnection (e.g. HVDC)(1, 2, 3, 4, 5)

High voltage direct current usedfor interconnection and long

distance transmission [24]7–9 Not relevant as already highest

TRL range

STATCOMs/SVCs(2, 3, 4, 5)

Static synchronouscompensator (STATCOMS) toprovide grid stability from the

supply side [25]

7–9 Not relevant as already highestTRL range

SCADA equip.(e.g. switches, sensors)

(2, 3, 4, 5)

Supervisory Control and DataAcquisition (SCADA) to control

whole networks of powergeneration assets, from a top

level down to the field level [26]

7–9 Not relevant as already highestTRL range

Nuclear(1) - 7–9 Not relevant as already highest

TRL rangeWind

(1) - 7–9 Not relevant as already highestTRL range

Biomass supply chain(1) - 7–9 Not relevant as already highest

TRL rangeHydrogen Storage

(1)Large scale storage capabilities

for hydrogen [27] 7–9 Not relevant as already highestTRL range

Hydrogen infrastructure(1, 2, 3, 4, 5) - 7–9 Not relevant as already highest

TRL rangeCombined Heat and

Power (CHP)(2, 3, 4, 5)

- 7–9 Not relevant as already highestTRL range

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3.1.2. Demand-Side Technologies Enabling Energy Service Models

Like the supply-side technologies, the demand-side technologies (see Table 4) are mainly in thedomain of smart appliances or energy efficiency. All technologies are seen to be at the stage wherethey can be widely deployed.

Table 4. TRLs of demand-side technologies.

Demand side technologies(Numbers indicate for which

business models these arerelevant: Lowcarbongenerators (1), New

Electrifiers (2), Energy ServiceCompanies (3), Peer to Peer

platform (4), Third PartyControllers (5).

Explanation(with literature reference for

not widely knowntechnologies)

Technology Readinesslevel (TRL)

(Colour Code outlinesTRL level)

1–3 red4–6 amber7–9 green

Constraints/Barriers/Limitsto increase TRL to 7–9

(Colour code illustrates howeasy to overcome)simple—green

medium—amberdifficult—red

Heat Pumps(2, 3, 5) For the electrification of heat 7–9 Not relevant as already highest

TRL rangeRemote controlled Electric

storage heaters(2, 3, 5)

For the storage of heat andits control 7–9 Not relevant as already highest

TRL range

Energy Efficient Lightning(1, 2, 3, 5)

For efficiency improvementdriven business models 7–9 Not relevant as already highest

TRL rangeIntelligent heating controllers

(2, 3, 5) For control of heat demands 7–9 Not relevant as already highestTRL range

In-home displays(2, 3, 4, 5)

For business models thatrequire human interaction

and response7–9 Not relevant as already highest

TRL range

Smart appliances(1, 2, 3, 4, 5) For Demand-Side Response 7–9 Not relevant as already highest

TRL rangeSmart EV Chargers

(1, 2, 3, 4, 5)For the electrification

of transport 7–9 Not relevant as already highestTRL range

Hydrogen Vehicles(1)

As one alternative for theelectrification of transport 7–9 Not relevant as already highest

TRL rangeHybrid Vehicles

(1, 2, 3, 5)As one alternative for theelectrification of transport 7–9 Not relevant as already highest

TRL range

Electric Vehicles(1, 2, 3, 5)

As one alternative for theelectrification of transport 7–9

Not relevant as already highestTRL range

3.1.3. Data and Energy System Integration Technologies Enabling Energy Service Models

In the case of Data and Energy Systems Integration (see Table 5), most of the technologies are at a stagewhere they can be deployed. However, according to the expert panel assessment, there are a number oftechnologies that still require further demonstrator projects before they can be widely deployed.

Table 5. TRLs of Data and Energy System Integration Technologies.

Technologies(Numbers indicate for which

business models these arerelevant: Low carbon generators(1), New Electrifiers (2), EnergyService Companies (3), Peer toPeer platform (4), Third PartyControllers (5). Crucial in bold

Explanation

(A)Technology Readiness

level (TRL)(Colour Code outlines

TRL level)1–3 red

4–6 amber7–9 green

(B)Constraints/Barriers/Limits

to increase TRL to 7–9(Colour code illustrates how

easy to overcome)simple—green

medium—amberdifficult—red

Smart Meter TechnologiesS(1, 2, 3, 4, 5) - 7–9 Not relevant as already highest

TRL rangeHome Energy Management

Systems (HEMS)(2, 3, 4, 5)

Operation of home energyassets with respect to use

and market [28]7–9 Not relevant as already highest

TRL range

Building Energy ManagementSystems (BEMS)

(2, 3, 4, 5)

Operation of building energyassets with respect to use

and market [29]7–9 Not relevant as already highest

TRL range

Demand-Side Response(1, 2, 3, 4, 5)

Flexible control of demand tomatch available supply [30] 7–9 Not relevant as already highest

TRL range

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Table 5. Cont.

Sensors (IoT)(2, 3, 5)

Different Internet of Thingscapable sensors necessary to

monitor the state of thedifferent energy assets

7–9 Not relevant as already highestTRL range

Vehicle-to-Grid Communication(V2G) 7–9 Not relevant as already highest

TRL rangeCommunication forWholesale market

Communication for real-timeelectricity markets [31] 4–6 medium

Cyber security - 7–9 Not relevant as already highestTRL range

Blockchain - 7–9 Not relevant as already highestTRL range

Peer-to-peer communication - 7–9 Not relevant as already highestTRL range

Reactive Power Control [32] 7–9 Not relevant as already highestTRL range

Local Network Balancing

Control technologies tobalance of Supply and

Demand on DistributionNetwork Level [33]

7–9 Not relevant as already highestTRL range

Peer-to-peer trading agents Automated participation inpeer-to-peer markets [34] 4–6 medium

Market/Trading platform

Trading platforms that linkwide ranges of data andtraders—from retail to

wholesale [34,35]

7–9 Not relevant as already highestTRL range

Trading Optimisation

Holistic solutions foroptimisation of trading

combined with electricityassets control [34]

7–9 Not relevant as already highestTRL range

Automated Payment Systems - 7–9 Not relevant as already highestTRL range

Car Sharing Systems - 7–9 Not relevant as already highestTRL range

Advanced DistributionManagement System (ADMS)

Software platform thatsupports distribution

management andoptimization. [36,37]

4–6 simple

Machine-Learning

General machine learningtechnologies to improve

autonomous operation ofenergy system

7–9 Not relevant as already highestTRL range

Machine-to-MachineCommunications

Communication betweenenergy assets to allow

cross-optimisation[38]

4–6 simple

Generation Optimisation General optimisation of thewhole generation park 4–6 medium

Big/Data processing & analysis General big data approachesto improve operation of assets 7–9 Not relevant as already highest

TRL range

Wide-Area Energy ManagementSystems (WAEMS)

Systems that can manage andoptimise energy assets on a

district level [39]4–6 simple

Virtual Power Plant (VPP)Aggregation of distributed

energy assets into onevirtual one [40]

4–6 medium

According to our respondents, there are three technologies where the barriers can be easilyovercome:

• Advanced Distribution Management Systems (ADMS): Software platform that supportsdistribution management and optimization [36]. There are already smaller trials that just needscaling up.

• Machine-to-Machine Communications: Communication between energy assets to allowcross-optimisation [38]. This type of technology is common in manufacturing, but has notbeen widely adopted to the energy sector yet.

• Wide-Area Energy Management Systems (WAEMS): Systems that can manage and optimiseenergy assets on a district level [39]. There are already smaller trials that just need scaling up.

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4. Discussion

These data show that there are very few barriers to business model innovation in the UK energysystem posed by supply- and demand-side energy technologies. They also show that there are somemoderate barriers to business model innovation posed by integrative ICT technologies, which atpresent, may be frustrating market entry or holding back business model innovation by remaininguntrialed at scale.

Adopting this approach to technology assessment allows the research team to explore whichtechnologies are holding back business model change, as opposed to assessing which individualtechnologies require cost reductions to enter the existing market. This is critical because the entryof new business models into an established market with substantial incumbent advantage putstechnological niches at a price and scale disadvantage [41]. This suggests that there is an opportunityto pursue technological trials, which focus less on individual engineering innovation and more oncombining and recombining ICT and systems integration tools to enable new business models to enterthe energy market. Specifically, there were four themes of areas that can be drawn from the date inwhich more work is necessary to allow new business models to flourish:

• Communication for wholesale markets: This allows the communication of wholesale marketinformation between any type of stakeholders, and also between wholesale markets [31]. Since thiswould require a large-scale demonstrator, this technology has not yet been deployed on a largerscale for energy markets.

• Peer-to-peer trading agents: This allows different types of energy market members to take partin transactions, including the current actors, as well as smaller actors, such as domestic customers.While peer-to-peer communication is widely used, for technologies, such as file-sharing, likeblockchain, the application of peer-to-peer trading agents, especially for the energy system, has notbeen demonstrated yet on a larger scale [42].

• Generation Optimisation: Currently, each generator optimizes their assets with respect to marketsignals and predictions. In large-scale generation optimization, generation assets could be used ina more efficient way through real-time optimization. As there is currently limited informationabout real-time demand forecasting, specifically at lower meter classifications, this has not beendemonstrated yet on larger scale [43].

• Virtual Power Plant: Aggregating a number of different generation, storage and demand assetsinto one heterogeneous distributed energy source through the use of cloud-based services in orderto take part as one in energy wholesale markets. There are encouraging developments in VPPsaccessing balancing and ancillary services markets [44].

4.1. Technology Gaps to be Overcome to Enable Future Business Models

In Section 3, we provided an overview of the relevant technologies for future electricity systems,their technology readiness levels and whether there are major barriers in the overcoming of these.In this section, we connect this back to the five energy system business model archetypes. For this,we outlined how crucial the technologies are, for these archetypes that are not yet in TRL 7—9(see Table 6).

First, the archetype Low carbon transmission capacity provider, which is the closest to the currentstatus quo, does not face any technological barriers at the moment. This is obvious, as this model doesnot require a change of the current centralized electricity market and system.

The archetypes New Electrifier, Serviced Home and Mobility and Third party control all implya transition to more decentralized and, due to the increased amount of actors, to a more automated energysystem. Therefore, the lack in maturity in technologies such as communication for wholesale markets,generation optimisation and virtual power plants play a crucial role for the implementation of such models.Currently, the technology, while deployed in trials, is not demonstrated on the larger scale.

Energies 2019, 12, 428 10 of 13

In comparison to these three archetypes, the archetype with the biggest challenge is the Peer toPeer 2.0 business archetype, as it faces the same issues as the latter three archetypes, as well as relieson the demonstration of a robust and reliable peer-to-peer trading infrastructure for energy markets.

To summarize, the technologies that have emerged as not being at a state of major deployment,are mainly integration and system technologies that require large scale demonstrators or deploymentto be of TRL 7—9. In contrast, the results from Section 3.1 show that none of the energy supply orenergy demand technologies require further research to make them technologically viable. We havenot assessed economic or political viability.

Table 6. Technological barriers that need to be overcome for future energy systems business models.

Survey results Archetypes

Technologies

(A)Technology

Readiness level(TRL)

(Colour Codeoutlines TRL level)

1-3 red4–6 amber7–9 green

(B)Constraints/

Barriers/Limitsto increase TRL

to 7–9(Colour code

illustrates howeasy to

overcome)simple—green

medium—amberdifficult—red

Low

carb

ontr

ansm

issi

onca

paci

typr

ovid

er

New

Elec

trifi

er

Serv

iced

Hom

ean

dM

obil

ity

Peer

toPe

er2.

0

Thi

rdpa

rty

cont

rol

Communication forWholesale market

[31]4–6 medium less crucial crucial crucial crucial

Peer-to-peertrading agents

[34]4–6 medium n/r n/r less crucial less

Adv. DistributionManagement

System (ADMS)[36]

4–6 simple less less less less less

M2MCommunications

[38]4–6 simple less less less less less

GenerationOptimisation 4–6 medium less crucial crucial crucial crucial

Wide-Area EnergyManagement

Systems (WAEMS)[39]

4–6 simple less crucial crucial less less

Virtual PowerPlant (VPP)

[40]4–6 medium less crucial crucial crucial crucial

1 Description of Archetype-Technology link: Colour code illustrates whether a technology creates a bottleneck forthe particular archetype, based upon (A) TRL, (B) how easy it can mature and (C) how relevant the technology is forthe archetype). Not relevant—no colour; less important/crucial technology that is ready, or can be made readyeasily—green; less important technology that is not ready yet, and that needs effort—light green; crucial technlogythat requires some effort to be ready—amber; crucial technology that requires significant effort to be made readyor that will not reach high TRLs—red.

5. Conclusion

In this work, we conducted an analysis of energy system technologies that will be needed forfuture energy system using an expert panel approach to obtain TRLs for key enabling elements of newbusiness models.

Our work shows the technologies that are needed to achieve the transition to novel businessmodels in future energy systems. Furthermore, we outline whether these are technologically mature.Here, we can conclude that most of the technologies are already at a stage that they can be deployedwidely, hence will not require research and development, nor deployment activities on a major scale.

However, there is still a set of energy system integration technologies that require majordemonstrators or deployment programs in order to reach higher TRLs. These are the technologies:

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Communication for wholesale markets, peer-to-peer trading agents, generation optimisation andvirtual power plant, all of which need a minimum scale to reach a higher maturity.

In order to do so, we recommend the creation of a set of large-scale demonstrators or one majordemonstrator where these technologies are deployed on a regional level. Ofgem’s Sandbox [45], the UKResearch and Innovation Smart local energy systems demonstrators the Energy System CatapultsEnergy Town programs could be suitable means in the UK.

Conversely, we believe that the other technologies listed in the results do not require significantfurther R&D investment by policy makers. We conclude that further basic research and developmentin these technologies is not critical to the transition towards sustainable future energy systems.

These twin statements, if verified by replicated studies, should lead to a new phase in supportingenergy systems innovation. It is not that further developments in, for example, offshore wind efficiencyor solar cell development are unlikely, it is simply that the returns on public innovation support inthese areas may be diminishing or at least outstripped by the potential for systems innovation thatsupport specific business models more towards the consumer end of the supply chain. These close tocustomer innovations such as peer-to-peer contracts and incentives for flexibility require a differentinnovation ecosystem described by iterative trials aimed at system optimisation.

Author Contributions: Christoph Mazur designed the study. Christoph Mazur, Jeffrey Hardy and Stephen Hallcollected the survey inputs and analyzed them. Christoph Mazur, Jeffrey Hardy, Stephen Hall and Mark Workmananalyzed the results, and wrote the paper.

Funding: This work was supported by the Energy Research Partnership and Energy Systems Catapult Utility2050 project. Part of this work was supported by the Engineering and Physical Sciences Research Council undergrant Ref: EP/N029488/1.

Acknowledgments: Thanks to the valuable feedback from the reviewers.

Conflicts of Interest: The authors declare no conflict of interest.

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